The Relationship of Climate Change and Malaria Incidence in the Gambella Region, Ethiopia

Abstract

Background: This study investigates the relationship between climate variables and malaria incidence in Ethiopia’s Gambella region, a hotspot for malaria transmission. Methods: Utilizing 30 years of satellite-derived climate data and 10 years of malaria incidence records from the Ethiopian Public Health Institute, this research analyzed trends and correlations. Climate variables, including rainfall, temperature, and relative humidity, were extracted using GPS data and global climate models from NASA. Autoregressive modeling was employed to assess the impact of these variables on malaria incidence at different time lags (lag 0, 1, and 2). Results: The analysis revealed significant upward trends in rainfall, relative humidity, and temperature over the 30-year period, coinciding with a rise in malaria cases over the past decade. Rainfall exhibited delayed effects on malaria incidence, while relative humidity demonstrated both immediate and persistent impacts. Relative humidity at lag 0 had the strongest influence (IRR = 1.002, 95% CI: 1.001–1.003), whereas temperature showed minimal effects (IRR = 1.000, 95% CI: 1.000–1.001). Conclusions: These findings underscore the critical role of climate variables in driving malaria transmission and highlight the urgent need for climate adaptation strategies, early warning systems, and strengthened health infrastructure. Leveraging climate data for predictive modeling and expanding targeted interventions, such as insecticide-treated nets (ITNs), is essential to mitigate climate-driven malaria risks and protect vulnerable communities in Gambella and similar regions

1. Introduction

Climate change is among the most significant global health threat of the 21st century [1,2,3,4] and refers to long-term changes in worldwide weather patterns [1,2,5]. Research indicates that 3.6 billion people currently reside in areas that are highly susceptible to the effects of climate change [6,7]. This vulnerability is expected to have dire consequences for global health. Between 2030 and 2050, models on climate change are projecting to result in approximately 250,000 additional deaths per year due to factors such as malaria, diarrhea, and heat stress [7,8,9,10].

Climate change directly and indirectly impacts health, including the spread of vector-borne diseases, respiratory illnesses, water scarcity, and forced migration [3,11,12,13,14]. Climate change significantly affects mosquito populations and disease transmission by altering habitats and life cycles. These impacts are further compounded by malaria patients’ demographics, such as age, treatment status, and mobility. Rising temperatures and changing precipitation patterns create favorable breeding conditions, accelerating development and expanding their geographic range into previously inhospitable areas [15,16]. Extreme weather events, such as floods, provide additional breeding sites, leading to increased populations and enhanced transmission of diseases like malaria [15,17,18]. Pollution and urbanization further contribute to health threats by disrupting ecosystems and increasing mosquito-breeding sites, especially in poorly managed urban environments [19,20]. Additionally, climate-induced food insecurity weakens immune systems in vulnerable populations, making them more susceptible to malaria infections [21].

In addition to natural and human-caused health stressors, climate change affects human health and leads to social disruption and migration and social upheaval compound health risks like malaria [22,23,24].

Vulnerable groups, including children, the elderly, those with preexisting health conditions [10,11,24,25], and low socioeconomic communities, face heightened risks from climate change and malaria due to varying levels of exposure, sensitivity, and adaptive capacities [25,26,27,28,29,30]. Increasingly unpredictable rainy seasons and shifting precipitation patterns varying by region in intensity, frequency, and timing have further contributed to the spread of malaria [18,20,30,31,32]. Malaria incidence is strongly influenced by environmental factors like temperature, precipitation, and humidity, which directly affect mosquito and parasite life cycles. Warmer temperatures (25–30 °C) accelerate mosquito development and parasite extrinsic incubation, boosting transmission rates [33,34,35,36]. Rainfall creates breeding sites, with prolonged or intense rainy seasons increasing transmission, while disrupted rainfall patterns, such as delays or reductions, also affect mosquito populations and malaria incidence. In Ethiopia, shifting precipitation patterns are altering the timing and intensity of malaria outbreaks [37,38,39].

Recent scientific evidence through meteorological station data has demonstrated that rainfall variability contributed to malaria epidemics [40,41,42,43]. These variations in rainfall contribute to a higher occurrence and severity of pests and diseases, including various tropical diseases like malaria, cholera, yellow fever, and meningitis [16,44,45]. These diseases are sensitive to changes in temperature, rainfall, and humidity, and alterations in the ecology of disease vectors lead to changes in the spatial and temporal patterns of transmission [17,46,47]. Also, climate changes affect the survival and distribution of disease vectors such as mosquitoes, potentially leading to an increase in diseases like malaria [11,17,26,48]. For example, mosquito belts have considerably expanded to higher elevations due to temperature increases, and hence malaria is expanding to highland areas that were formerly malaria-free [17,49].

In Ethiopia, climate change exacerbates existing health challenges, including extreme climates, droughts, floods, increased temperatures, and erratic rainfall [50,51,52]. These disruptions have led to an increase in respiratory and waterborne illnesses, infectious diseases, and threats to mental health [52,53,54]. Efforts have been made to assess the impacts of climate change on the incidence of infectious diseases, including reducing greenhouse gas emissions, improving infrastructure, and strengthening public health systems to respond effectively to climate-related health threats [6,48,55]. Also, national initiatives on climate change and malaria prioritize early warning systems, adaptive vector control, climate-resilient health systems, and evidence-based policymaking. In Ethiopia, these efforts are integrated into broader climate adaptation frameworks. The National Meteorological Agency collaborates with the health sector for early malaria outbreak predictions, while the National Malaria Control Program (NMCP) incorporates climate data into its five-year strategic plan to enhance malaria prevention and control [2,7,16,56,57]. Recognizing climate change as a crucial health issue, the World Health Organization (WHO) emphasizes the need for health to be a central consideration in climate policies [20,55,56]. Individual and collective actions are also used to mitigate the impacts of climate change on health [2,3,14,42,57,58]. Recognizing the above effect, there is an increasing need to adapt healthcare systems to respond to new malaria dynamics, especially in regions newly affected by the disease. However, currently available studies are insufficient, such as lack of long-term data, regional-specific analyses to influence decision-makers to prioritize mitigation, and adaptation strategies for reducing the impact of climate change on the incidence of malaria in Ethiopia. Given the Gambella region’s proximity to South Sudan, the study area can be a potential hotspot for cross-border malaria transmission [35,40,59,60]. Therefore, the current study investigated the potential relationships between climate variables change and the incidence of malaria in the Gambella region for the past years. The study findings will be used for researchers to conduct further studies. Also, it can be used by the Ministry of Health and EPHI to develop climate-smart policies to reduce the impact of climate-sensitive diseases in the current changing climate.

In Ethiopia, the national malaria early warning system integrates climate, weather, and environmental data, utilizing tools like EPIDEMIA for disease monitoring. The Ethiopian Public Health Institute (EPHI), through its Public Health Emergency Management (PHEM) Center, strengthens early warning and response systems for malaria and other climate-sensitive diseases. EPHI’s National Data Management Center (NDMC) supports evidence-based decision-making by processing health data and research for the Federal Ministry of Health.

2. Materials and Methods

2.1. Setting and Study Design

This study, conducted in the Gambella region from March to April 2023, focused on its four administrative zones (Agnewak, Nuwer, Mezhenger, and Etang Special Zone) and 13 districts, located near the borders of South Sudan, Oromia, and SNNPR. The Gambella region in southwestern Ethiopia is divided into 13 districts, each with distinct characteristics regarding size, population, and ethnic composition. Covering approximately 29,782 square kilometers, it is home to around 500,000 people, including ethnic groups such as the Nuer, Anuak, and Majang (CSA, 2007; FDRE, 2014). Districts like Itang and Gambella Zuria are among the most populous and are located near the South Sudanese border, fostering strong cultural and economic ties with neighboring communities. Similarly, Akobo and Jikawo are predominantly inhabited by the Nuer, reflecting the influence of shared ethnic identities on regional dynamics (UNHCR, 2021). In contrast, Godere and Mengesh are more remote and primarily home to the Majang people, known for their unique culture and connection to the forest ecosystem. Smaller districts like Lare and Abobo have lower population densities but contribute to agricultural productivity, while larger districts, such as Gog, cover vast areas with sparse populations, and more compact districts like Jor are densely settled (FDRE, 2014). The region’s proximity to South Sudan significantly impacts its socio-political and economic landscape, with frequent cross-border movements, trade, and a notable presence of South Sudanese refugees, underscoring its role as a humanitarian and cultural hub (UNHCR, 2021). Three districts (Itang, Godere, and Akobo) were selected for this study.

Itang is one of the most densely populated districts in the Gambella region, situated close to the South Sudanese border. This strategic location fosters strong cultural and economic ties with neighboring communities, making it a vital hub for cross-border trade and movement. The district is characterized by its high population density, ethnic diversity—including the Nuer and Anuak peoples—and significant socio-economic importance due to its borderland position.

In contrast, Godere is a more remote district, primarily home to the Majang people, who are renowned for their unique cultural traditions and deep connection to the forest ecosystem. This district exemplifies the region’s ecological and cultural diversity, with its remote setting, lower population density, and a distinct ethnic identity deeply rooted in the forest environment.

Akobo, on the other hand, is predominantly inhabited by the Nuer people, underscoring the influence of shared ethnic identities on regional dynamics. Its proximity to South Sudan also highlights the district’s socio-political and humanitarian significance, as it plays a key role in addressing cross-border movements and refugee-related challenges. Together, these districts showcase the Gambella Region’s rich diversity in terms of population, culture, and geography as shown in Figure 1.

2.2. Data Sources, Collection Tools, and Procedures

The data on climate variables and incidence of malaria were collected using a retrospective cross-sectional study design. Study sites were selected based on EPHI climate-sensitive disease surveillance sites in the Gambella region. Data on climate variability (rainfall, temperature, and relative humidity) were extracted from the Global Land Data Assimilation (GLDAS, version resolution) and validated against ground-based observations, To, and reanalysis system of NASA’s Goddard Space Flight Center (GSFC) for the period between 1992 and 2021) [61]. To represent districts in terms of climate variability, three geographical points (Itang, Godere, and Akobo) in surveillance sites were chosen with different altitudes. Climate data were extracted from satellite sources using the geographical coordinates of each location. Specific humidity was converted into relative humidity using altitude, air pressure, and average temperature. The average monthly value of each climatic variable at each location was calculated and used to investigate the relationship with monthly malaria incidences. The satellite datasets used to obtain climatic variability data were validated and published by other research groups [52]. Incidence data for climate-sensitive diseases in health facilities were recorded based on WHO and national guidelines for the last 10 years (2012–2022) on a monthly basis. Confirmed malaria case data were collected from EPHI climate-sensitive disease surveillance and aggregated to a monthly and yearly basis. Precautions were taken before, during, and after data collection to ensure the reliability and validity of the data. Before conducting any analysis, thorough data cleaning was performed on time-sensitive data, and potential outliers were excluded from the analysis. Additionally, all necessary assumptions were carefully reviewed during the advanced analysis of the data.

2.3. Data Processing and Analysis

Descriptive statistics and range were utilized to describe the variability of rainfall, precipitation, and humidity. Furthermore, the average rate of climate-sensitive incidents, along with a 95% confidence interval (CI), was calculated for each month. The analysis was carried out using time series analysis for the period 2012–2021. Satellite climate data from nineteen health facility locations were accessed, and a trend analysis was conducted using a Stata Mann–Kendall (MK 18) trend analysis test [62]. The MK trend test is a widely employed method for identifying climate trends in time series data. The MK test was implemented using Stata software, and the results were interpreted to determine whether significant increasing or decreasing trends existed in the climate variables and disease incidence over time. To explore the relationships between climate variables and malaria incidence, Spearman’s rank correlation coefficient was used. Spearman’s correlation is a non-parametric measure that assesses the strength and direction of the association between two continuous variables. Also, correlation analysis using Spearman’s correlation coefficient to investigate the crude associations between climate-sensitive diseases and the lag effect of climatic factors (lag zero, one, and two) was utilized. Correlation analysis was conducted to investigate both the immediate (lag 0) and delayed effects (lag 1 and lag 2) of climatic factors on malaria incidence. Lag 0 represents the current month, lag 1 represents the previous month, and lag 2 represents two months prior. This approach allows for the examination of potential delayed impacts of climate variables on disease incidence. Additionally, due to the dispersed nature of the malaria incidence data and its over-dispersion (where the variance exceeds the mean), negative binomial regression was chosen as the primary modeling approach. Negative binomial regression analysis was chosen over other models because the mean of the count is lesser than the variance of the count; then, negative binomial regression was used, and the model was used mostly for over-dispersed count outcome variables.

We employed bivariate and multivariable negative binomial regression analysis to explore the relationship between climatic factors and malaria incidences. The results were reported using the incidence rate ratio with a 95% confidence interval and a significance level of p < 0.05. The use of IRR is justified because it provides an intuitive measure of the relative change in disease incidence associated with changes in climate variables, making it easier to interpret the public health implications of the findings. To assess multicollinearity among the independent variables, we conducted a Variance Inflation Factor (VIF) test.

3. Results

3.1. Trends of Climate Variables Change in Gambella Region

Annual Temperature Linear Trend Analysis:

This study shows a 0.35 °C increase in average temperature in the Gambella region over the 30 years from 1992 to 2021, with an annual increase of 0.012 °C. This is statistically significant (p < 0.05) and highlights a clear warming trend in the region. Based on data collected from 19 health facilities, an annual temperature linear trend analysis is presented below. (Figure 2 illustrates the significant upward trends in temperature over the 30-year study period).

• Analysis of linear trends of rainfall:

This study observed a steady increase in rainfall over the same 30-year period, reflecting notable year-to-year variability (slope of the trend line is 6.6309). This increasing rainfall trend is visually confirmed through linear trend analysis. The data gathered from 19 health facility surveillance sites show that there is a clear upward trend in rainfall. The figure below visually represents this trend and displays the year-to-year variations in rainfall, as well as the notable increase in clusters (see Figure 3).

• Linear Trends of relative humidity:

Over the 30-year period, the average relative humidity in Gambella increased by 2.38%, with an annual rise of 0.08%. The data show fluctuations in humidity levels, with some years experiencing higher or lower relative humidity compared to the long-term average (e.g., the period 2000–2011 was drier). This trend (slope of the trend line is 0.01) reflects the growing humidity levels in the region. Figure 4 below provides a summary of the relative humidity trends in the study area from 1992 to 2021.

3.2. Malaria Incidence

From 2012 to 2022, a total of 1,147,205 malaria cases were reported across 19 surveillance sites in Gambella. Malaria incidence showed fluctuations, with increases in specific years (e.g., 2013–2014, 2015–2017, 2020–2022) and decreases in others (e.g., 2014–2015, 2017–2019). As shown in Figure 5 below, the overall trend shows a linear increase in malaria cases over the observed decade, suggesting a rise in malaria transmission despite year-to-year variations.

3.3. Effects of Climate Variables on Malaria Incidence

This study examined the relationship between three meteorological variables relative humidity, rainfall, and temperature malaria incidence.

3.3.1. Temperature

There was a weak negative correlation between temperature and the response variable at Lag 0 months (Spearman’s r = −0.0482, p = 0.432), with no statistical significance. Similarly, at Lag 1 month, the correlation was also insignificant (Spearman’s r = −0.0518, p = 0.657). At Lag 2 months, a weak positive correlation was observed (Spearman’s r = 0.0408, p = 0.121), which remained statistically insignificant.

3.3.2. Rainfall

The linear trend line with a slope of 6.6309 suggests that average rainfall has been increasing over the years from 1992 to 2021. Rainfall demonstrated a significant delayed impact on malaria incidence. Lag 0 months: A weak but statistically significant positive correlation was observed (Spearman’s r = 0.2276, p = 0.003), indicating that higher rainfall is associated with an increase in malaria cases during the same month. Lag 1 month: A moderate positive and statistically significant correlation (Spearman’s r = 0.4262, p = 0.001) suggested that rainfall from one month prior had a stronger effect on malaria incidence. Lag 2 months: The correlation remained moderate and significant (Spearman’s r = 0.3861, p = 0.005), showing that rainfall from two months prior continued to influence malaria incidence.

3.3.3. Relative Humidity

Relative humidity displayed a significant immediate impact, with its influence persisting across all lag periods. Lag 0 months: A strong positive and statistically significant correlation was observed (Spearman’s r = 0.5158, p = 0.027), indicating that higher relative humidity is concomitant with malaria incidence increase during the same month. Lag 1 month: A moderate positive and statistically significant correlation was found (Spearman’s r = 0.3567, p = 0.011), reflecting a lingering effect of relative humidity one month prior. Lag 2 months: The correlation strengthened again (Spearman’s r = 0.4262, p = 0.001), maintaining moderate significance at a two-month lag.

Overall, the findings highlight that rainfall and relative humidity are significant predictors of malaria incidence, with rainfall showing a delayed effect and relative humidity demonstrating both immediate and persistent impacts in Gambella region. In contrast, temperature does not exhibit a significant relationship with malaria incidence across all lag periods in this context.

Table 1. Estimated correlation between monthly malaria cases and mean monthly climatic variables in Gambella region, Ethiopia (2023).

Monthly Mean Climate Variable	Lag Months	Spearman’s r	p Value
Temperature in °C	0 months	−0.0482	0.432
	1 months	−0.0518	0.657
	2 months	0.0408	0.121
Rainfall in mm	0 months	0.2276	0.003
	1 months	0.4262	0.001
	2 months	0.3861	0.005
Relative humidity	0 months	0.5158	0.027
	1 months	0.3567	0.011
	2 months	0.4262	0.001

below summarizes climate variable, lag months, and Spearman’s r with p value.

3.4. Multivariate Regression Analysis

The negative binomial regression analysis confirmed the relationship between climate variables and malaria incidence. Temperature was statistically significant at lag 0, with an incidence rate ratio (IRR) of 1.000 (95% CI: 1.000–1.001, p < 0.05), indicating a minimal but significant impact on malaria morbidity. Relative humidity was significant at all lag periods, with the highest IRR at lag 0 (IRR = 1.002, 95% CI: 1.001–1.003, p < 0.05). Rainfall was also significant across all lag periods, with IRRs ranging from 1.001 to 1.003 (p < 0.05), confirming its contribution to rising malaria cases. The Variance Inflation Factor (VIF) test was performed to assess multicollinearity among the independent variables. In this study, all variables, including the mean VIF, all variables showed VIF values below the threshold of 10, with a mean VIF of 1.09, indicating no significant multicollinearity in the dataset.

Table 2. Negative binomial regression analysis of the effect of climate variability on malaria incidence in the Gambella region, Ethiopia (2023).

Climate Variables	Lag	Crude IRR (95% CI)	Adjusted IRR (95% CI)
Monthly mean Temperature	0 months	1.000 (0.997, 1.002) *	1.000 (1.000, 1.001) *
	1 months	0.999 (0.997, 1.002)	0.998 (0.996,1.000)
	2 months	0.999(0.996,1.001)	0.998(0.996,1.000)
Monthly mean Relative humidity	0 months	1.002 (1.001, 1.003) *	1.002 (1.001, 1.003) *
	1 months	1.001(1.000,1.002) *	1.002(1.001,1.003) *
	2 months	1.001(1.000,1.002) *	1.001(1.000, 1.002) *
Monthly mean Rainfall	0 months	1.001 (1.000, 1.002) *	1.001(1.000, 1.002) *
	1 months	1.002(1.001,1.003) *	1.001 (1.0001,1.003) *
	2 months	1.002 (1.001, 1.003) *	1.002(1.001,1.003) *

* Statistically significant at p value 0.05.

below summarizes the results of the negative binomial regression analysis, which examined the impact of climate variability on malaria morbidity in the Gambella region of Ethiopia.

4. Discussion

This study highlights the intricate relationship between climate change and malaria incidence, emphasizing the role of temperature, rainfall, and relative humidity in shaping malaria transmission dynamics. Consistent with existing research, rising temperatures, particularly in previously cooler regions, have expanded malaria-prone areas by creating favorable conditions for mosquito breeding and parasite development [2,30,42,44,57]. Over the past three decades, the Gambella region has experienced significant increases in temperature, rainfall, and fluctuated relative humidity as key factors influencing malaria transmission. These upward trends have been strongly associated with increased malaria cases, particularly at lags of 0, 1, and 2 months, where humidity and rainfall demonstrated strong positive correlations with malaria incidence. This aligns with prior studies showing that malaria transmission is highly sensitive to climate variability, particularly in tropical regions [1,37,44,51]. The findings of this study reveal a nuanced relationship between climate variables and malaria incidence. Moderate increases in rainfall and humidity were positively associated with higher malaria cases, likely due to optimal mosquito-breeding conditions. However, extreme climatic events, such as prolonged droughts or flooding resulting from excessive rainfall can disrupt mosquito habitats, temporarily reducing transmission. This finding also concurred with previous studies [45,48,53,57]. This underscores the importance of localized, evidence-based strategies tailored to address the varying impacts of climate on malaria dynamics.

Rainfall and relative humidity emerged as the strongest predictors of malaria incidence. Rainfall showed a particularly strong positive association with malaria cases at a lag of 1 to 2 months. This can be attributed to rainfall creating mosquito-breeding sites, while higher humidity extends mosquito lifespans, increasing transmission likelihood. The observed correlation supports previous studies demonstrating the significant role of rainfall variability in driving malaria outbreaks [6,24,47,50,60]. These findings suggest that monitoring rainfall and humidity patterns could serve as an effective early warning system for malaria outbreaks, enabling timely public health interventions. The variability in climate patterns, including erratic rainfall and temperature fluctuations, presents significant challenges for public health systems in malaria-endemic regions like Gambella. Without effective adaptation strategies, these climatic changes are expected to exacerbate the malaria burden. Similar trends have been documented in other malaria-prone regions, where shifting seasonal rainfall and temperature patterns have been linked to unpredictable malaria transmission cycles [12,18,39,60]. This study highlights the critical need to integrate climate data into malaria surveillance systems to enhance the effectiveness of control strategies [23,28,29,30,39].

Temperature trends in Gambella provide further insight. The region’s average temperature has risen by 0.012 °C annually over the past 30 years. Although the immediate effect of temperature on malaria incidence was less pronounced compared to rainfall and humidity, the long-term warming trend has contributed to conditions more conducive to malaria vector proliferation. Warmer temperatures accelerate mosquito larval development and the lifecycle of the Plasmodium parasite, enhancing transmission potential. This is consistent with global research showing that even slight temperature increases can expand malaria transmission into previously unaffected areas, including highland regions [1,3,40,46,48]. This finding also lowered as compared some other findings; this might be due to geographic and study period differences [37]. Additionally, temperature influences mosquito survival rates and parasite development, reinforcing its indirect but critical role in malaria dynamics [22,26,28,43,54,56].

The primary malaria vector in Gambella is Anopheles arabiensis, with secondary vectors including An. funestus, An. pharoensis, An. nili, and An. coustani [36]. Temperature, ranging from 17.3 to 33 °C, optimizes mosquito survival, development, and malaria parasite transmission. High rainfall (900–2100 mm annually) and humidity during the wet season (May–October) create breeding sites and extend mosquito lifespans, leading to increased malaria incidence from June to October, peaking in July–September. Climate change may further alter mosquito distribution and population dynamics, exacerbating malaria transmission [20,33]. These insights are critical for optimizing vector control strategies, such as timing indoor residual spraying and insecticide-treated net distribution, to align with seasonal mosquito abundance and transmission patterns [31,34,35,51]. The effectiveness of malaria control programs hinges on interventions like insecticide-treated nets, indoor residual spraying, and improved healthcare access. However, environmental factors, such as ponds and wetlands, can create breeding grounds for Anopheles mosquitoes, undermining these efforts if not managed properly [35,38,52]. Additionally, the rise in insecticide resistance among these mosquitoes poses a significant challenge to sustainability. Human behavior, including outdoor activities at night and varying exposure to bites, further complicates control efforts. Addressing these interconnected issues requires integrated, adaptive strategies that combine environmental management, innovative vector control, and community behavior change to ensure long-term success in malaria prevention and elimination [12,15,35].

Developing an early warning system that incorporates climate forecasts could help health authorities to integrate in existing systems, anticipate, and prepare for outbreaks, thereby reducing the disease burden. Despite the strong association between climate variables and malaria incidence, there is a lack of comprehensive measures to mitigate the health risks posed by climate change and malaria [10,14,27,52,54,60]. A recent projection study across Ethiopia indicated an upward trajectory in climate variables across all regions, further underscoring the growing impact of climate variability on malaria prevalence [10,43,47,60]. These findings stress the urgency of incorporating climate adaptation measures into national and regional malaria control strategies. Strengthening climate-informed early warning systems, enhancing community-based interventions, and investing in climate-resilient health infrastructure are critical steps to mitigate the health impacts of climate change. Allocating resources to regions most vulnerable to climate-driven malaria outbreaks will be essential for ensuring equity and effectiveness in intervention efforts [18,38,46,56,59].

Finally, this study identifies gaps in data availability and methodological challenges in establishing causality between climate change and malaria trends. Variability in the quality and resolution of climate and epidemiological datasets limits the precision of predictive and relationships models. Additionally, confounding factors such as migration, urbanization, and agricultural practices complicate the relationship between climate variables and malaria incidence, necessitating interdisciplinary approaches to better understand and address malaria dynamics [6,44,54,55].

Limitations

This study is subject to several limitations that should be addressed in future research. First, the use of monthly average morbidity data and climate variables may obscure the finer temporal relationships between climate fluctuations and malaria transmission. Future studies should consider more granular data, such as daily or weekly records, to capture the immediate effects of climate variability on malaria outbreaks. Additionally, this study did not account for confounding factors such as socioeconomic status, behavioral practices, or access to healthcare, which could influence malaria incidence. The analysis is based on secondary data collected retrospectively. So other variables that might be confounders were not captured in the data sources. In the future, using prospective study, efforts can be performed to control potential confounders either during design stage or analysis stage. Incorporating these variables into future analyses would provide a more comprehensive understanding of the climate–malaria relationship.

5. Conclusions

The fight against malaria in Africa faces significant challenges from climate change, but recent advancements offer actionable solutions to mitigate its impact. Therefore, this study provides compelling evidence of the link between climate change and the increasing incidence of malaria in the Gambella region of Ethiopia. The rising trends in temperature, rainfall, and humidity over the past three decades have created more favorable conditions for malaria transmission, highlighting the need for urgent adaptation measures. This study found statistically significant associations between malaria cases and both rainfall and humidity at different time lags, suggesting that climate variability has a direct influence on the disease’s transmission dynamics. To mitigate the increasing risk of malaria under changing climate conditions, it is recommended that the Ethiopian government strengthen its policy frameworks and invest in climate-adaptive health infrastructure: (i) Implement key strategies in deploying next generation insecticide treated nets (ITNs) to combat insecticide resistance, integrating effective vaccines into immunization programs, and establishing genomic surveillance networks to monitor resistant strains. (ii) Development of Climate-Integrated Early Warning Systems: Leveraging climate data such as temperature, rainfall, and humidity for real-time malaria outbreak predictions could improve preventive measures. (iii) Strengthening Health Infrastructure: Increasing the capacity of health facilities to respond to malaria outbreaks, particularly in areas with heightened climate sensitivity, should be prioritized. (iv) Community-Based Awareness and Prevention Programs:

Also, strengthening climate-resilient health infrastructure and developing early warning systems can enhance outbreak prediction and response, while fostering community-led prevention programs ensures sustainable and culturally appropriate solutions.

Further research is needed to refine the understanding of how climate variability influences malaria dynamics (including socioeconomic status, behavioral practices, or access to healthcare) and to develop more effective mitigation strategies. Long-term planning and more extensive research are essential to understanding the full extent of climate change’s impact on public health, particularly in malaria-prone regions like Gambella.